Additive Attention in Convolutional Networks
It seems that in the reference you provided they use attention to compute weights for the encoder representations based on the context provided by the decoder (Fig. 1). As far as I can tell, attention is applied after convolution (in fact, after the GLU step), so it does not affect the feature maps directly. Rather, attention is used to select the target words in the decoder. In other papers (e.g., this one), attention is applied directly to the feature maps in a way that is more similar to what you described.

Regarding your second question, the paper you reference actually provides a link to the source code they used. It is written in Lua (using Torch) instead of PyTorch, probably because PyTorch development was just starting when the paper was published. At any rate, you should be able to follow the Lua code and translate it into PyTorch.